Sliding-mode PID Control of UAV Based on Particle Swarm Parameter Tuning

Liu, Yunping; Yan, Xiu‐Ping; Yan, Fei; Xu, Ze Yin; Shang, Weiyan

doi:10.32604/cmc.2020.05746

Cited by 13 publications

(4 citation statements)

References 11 publications

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“…1, the train condition changes frequently, and the long time is close to the limit speed operation. Also, there are security risks, and the train operation security is reduced, such a control strategy is not suitable for the train control [15][16][17][18][19].…”

Section: Simulation Experiments Results and Analysismentioning

confidence: 99%

Operation Optimal Control of Urban Rail Train Based on Multi-Objective Particle Swarm Optimization

Liang¹,

Qi²,

Liang³

2022

Computer Systems Science and Engineering

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The energy consumption of train operation occupies a large proportion of the total consumption of railway transportation. In order to improve the operating energy utilization rate of trains, a multi-objective particle swarm optimization (MPSO) algorithm with energy consumption, punctuality and parking accuracy as the objective and safety as the constraint is built. To accelerate its the convergence process, the train operation progression is divided into several modes according to the train speed-distance curve. A human-computer interactive particle swarm optimization algorithm is proposed, which presents the optimized results after a certain number of iterations to the decision maker, and the satisfactory outcomes can be obtained after a limited number of adjustments. The multiobjective particle swarm optimization (MPSO) algorithm is used to optimize the train operation process. An algorithm based on the important relationship between the objective and the preference information of the given reference points is suggested to overcome the shortcomings of the existing algorithms. These methods significantly increase the computational complexity and convergence of the algorithm. An adaptive fuzzy logic system that can simultaneously utilize experience information and field data information is proposed to adjust the consequences of off-line optimization in real time, thereby eliminating the influence of uncertainty on train operation. After optimization and adjustment, the whole running time has been increased by 0.5 s, the energy consumption has been reduced by 12%, the parking accuracy has been increased by 8%, and the comprehensive performance has been enhanced.

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Section: Simulation Experiments Results and Analysismentioning

confidence: 99%

Operation Optimal Control of Urban Rail Train Based on Multi-Objective Particle Swarm Optimization

Liang¹,

Qi²,

Liang³

2022

Computer Systems Science and Engineering

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“…Therefore, many researchers have used PSO algorithms in their applications for solving all optimization problems. PSO algorithm is used to build a feasible path, in order to optimize in terms of fuel and threat cost for autonomous UAV 3D path planning [20], pose estimation by optimizing boxes properties [21], improve stability for UAV manipulator control system such as in [22][23][24]. In [25], PSO based  ISSN: 2252-8938 Int J Artif Intell, Vol.…”

Section: Figure 1 Ttc Definition For Quadcopter Platformmentioning

confidence: 99%

Modelling of time-to collision for unmanned aerial vehicle using particles swarm optimization

Sabikan

Nawawi

Aziz

2020

IJ-AI

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A method for the development of Time-to-Collision (TTC) mathematical model for outdoor Unmanned Aerial Vehicle (UAV) using Particles Swarm Optimization (PSO), are presented. TTC is the time required for a UAV either to collide with any static obstacle or completely stop without applying any braking control system when the throttle is fully released. This model provides predictions of time before UAV will collide with the obstacle in the same path based on their parameter, for instance, current speed and payload. However, this paper focus on the methodology of the implementation of PSO to develop the TTC model for 5 different set of payloads. This work utilizes a quadcopter as our testbed system, that equipped with a Global Positioning System (GPS) receiver unit, a flight controller with data recording capability and ground control station for real-time monitoring. The recorded onboard flight mission data for 5 different set of payloads has been analyzed to develop a mathematical model of TTC through the PSO approach. The horizontal ground speed, throttle magnitudes and flight time stamp are extracted from the on-board quadcopter flight mission. PSO algorithm is used to find the optimal linear TTC model function, while the mean square error is used to evaluate the best fitness of the solution. The results of the TTC mathematical model for each payload are described.

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“…Therefore, several robust control system design methods are developed for uncertain systems which has followed several directions. Sliding mode is one of those control directions which acts against external disturbances of linear or non-linear systems [1,2]. Linear matrix inequalities methods are applied to interval systems with parameter uncertainties [3,4].…”

Section: Introductionmentioning

confidence: 99%

Fractional Order Linear Active Disturbance Rejection Control for Linear Flexible Joint System

Mehedi¹,

Mansouri²,

Al‐Saggaf³

et al. 2022

Computers, Materials &Amp; Continua

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A linear flexible joint system using fractional order linear active disturbance rejection control is studied in this paper. With this control scheme, the performance against disturbances, uncertainties, and attenuation is enhanced. Linear active disturbance rejection control (LADRC) is mainly based on an extended state observer (ESO) technology. A fractional integral (FOI) action is combined with the LADRC technique which proposes a hybrid control scheme like FO-LADRC. Incorporating this FOI action improves the robustness of the standard LADRC. The set-point tracking of the proposed FO-LADRC scheme is designed by Bode's ideal transfer function (BITF) based robust closed-loop concept, an appropriate pole placement method. The effectiveness of the proposed FO-LADRC scheme is illustrated through experimental results on the linear flexible joint system (LFJS). The results show the enhancement of the robustness with disturbance rejection. Furthermore, a comparative analysis is presented with the results obtained using the integer-order LADRC and FO-LADRC scheme.

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Sliding-mode PID Control of UAV Based on Particle Swarm Parameter Tuning

Cited by 13 publications

References 11 publications

Operation Optimal Control of Urban Rail Train Based on Multi-Objective Particle Swarm Optimization

Operation Optimal Control of Urban Rail Train Based on Multi-Objective Particle Swarm Optimization

Modelling of time-to collision for unmanned aerial vehicle using particles swarm optimization

Fractional Order Linear Active Disturbance Rejection Control for Linear Flexible Joint System

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